Exploring AI Agent Examples & Use Cases to Transform Customer Experience

Summary: Understanding AI agents with practical examples

• AI agents for call centers connect systems and automate support workflows, reducing manual handoffs between tools.
• Agentic AI use cases include understanding customer intent and completing multi-step actions like updating accounts, processing refunds, or retrieving CRM data.
• Voice AI agents and chat AI systems handle routine customer requests such as order tracking, billing questions, and appointment scheduling.
• AI agents examples also include real-time assistance for human agents, surfacing knowledge and guidance during conversations.
• AI agents analyze interactions and automate post-call work, including quality scoring, customer sentiment analysis, and CRM documentation.

The most practical AI agents in call centers were built to handle one task at a time. A bot answers FAQs. A separate tool logs tickets. A QA team manually reviews a fraction of calls. No single tool is the failure point. The failure is that none of them connect, so agents burn time on hand-offs and managers make decisions from an incomplete picture.

An IBM study of over 3,500 senior executives found that 92% of leaders expect agentic AI to deliver measurable ROI. Contact centers are one of the clearest places to see this impact.

AI agents can interpret customer intent, connect multiple decisions, and take action automatically. For example, they can process refunds, update account details, and pull records from connected systems without waiting for human sign off at every step.

However, these systems are designed to work alongside customer support reps, not replace them. Every deployment should also include a clear escalation path to a human agent for judgment calls, sensitive conversations, or situations that fall outside the defined scope.This guide covers the most practical call center use cases, what measurable results look like, what to evaluate when choosing a platform, and how five leading platforms compare.

The highest-impact deployments tend to share a few traits:

• High call volume with a large share of repeatable request types
• Clear policies that define when an agent can act and when it must escalate
• Connected systems, CRM, order management, and knowledge bases, that the agent can read from and write to
• QA processes that cover more than the 1-2% of calls a human team can realistically review

What is the difference between agentic AI and other AI?

Most AI tools in contact centers fall into two categories: rule-based chatbots that follow decision trees, and generative AI tools or virtual agents that produce responses but leave a human to decide what to do with them. Both stop short of taking action, which means a rep still has to read the output, switch to another system, and complete the request manually. 

Agentic AI closes that gap by combining intent detection, multi-step planning, and direct execution within connected tools. A single customer request, say a billing dispute tied to a recent order, can require pulling data from a CRM, checking a payment system, and updating a ticket, and an agentic AI handles that whole chain without waiting for human sign-off at each step.

What are the use cases of AI agents for large enterprises?

AI agents are being deployed at specific points in the contact center workflow where volume is high, tasks are repeatable, and speed matters.

1. Voice and chat AI agents handling inbound requests

AI agents handle inbound voice and chat by identifying what a customer needs, pulling relevant data from connected systems - CRM, order management, knowledge bases - and resolving the request without transferring to a live rep.

Common examples include order status checks, appointment scheduling, returns processing, product questions, account verification, and billing updates.

This reduces queue wait times on high-volume, routine interactions, though it requires accurate speech recognition and well-defined handoff protocols for cases the agent cannot resolve.

2. AI agents assisting live reps during conversations

Some AI agents do not interact with customers at all. They work in the background during live conversations, identifying intent and emotional cues, searching internal systems, and surfacing relevant knowledge articles, policy information, and suggested responses directly to the rep. This improves handle time and first-call resolution without the customer knowing AI is involved. The rep stays in control of the conversation while the AI handles the information retrieval that would otherwise mean putting the customer on hold.

3. Automated quality scoring of every interaction

AI agents evaluate every customer interaction against quality standards, identify coaching opportunities, and flag compliance violations as they occur.

Traditional QA processes review only 1-2% of calls. AI scoring closes that gap by covering 100% of interactions, though it requires custom scorecards built around actual business standards rather than generic criteria.

4. Generating customer satisfaction scores without surveys

AI agents infer customer satisfaction by analyzing tone, word choice, and conversation patterns across every interaction - without waiting for a customer to complete a post-call survey. This works when models are trained on contact center conversation data rather than general sentiment data, which tends to miss domain-specific language and context. During implementation, results should be validated against actual survey responses to confirm that the model is calibrated correctly.

5. Automating After-Call Documentation

After a call ends, AI agents generate summaries, populate CRM fields, create follow-up tasks, and log interaction outcomes - removing the manual data entry that extends handle time and delays reporting. This requires the model to understand company-specific terminology and the structure of the CRM schema it is writing to; otherwise, outputs need significant correction before they are usable.

What Are Some Examples Of AI Agents?

AI agents are being deployed in industries where customer interactions are high-volume, time-sensitive, and tied to backend systems that a human rep would otherwise have to navigate manually. The examples below cover four of those industries, with the specific tasks AI agents handle in each one.

1. AI Virtual Agents in Retail

Retail contact centers deal with two distinct problem types: pre-purchase questions and post-purchase issues. An AI agent can handle both without transferring the customer, pulling product data, order status, and return policy details from connected systems in the same interaction.

• Shopping assistant: Answers questions about features, sizing, pricing, and availability, and surfaces relevant options when a customer describes what they need in plain language.
• Post-purchase support: Handles order tracking, returns processing, and basic installation guidance by pulling data from order management and CRM systems directly.
• Smart escalation: When a query becomes complex or emotionally charged, the agent transfers to a human rep with the full conversation history attached, so the customer does not repeat themselves.

2. AI Virtual Agents in Healthcare

Healthcare organizations use AI agents to take administrative workload off front-desk and billing staff. The agents handle the repeatable, rules-driven tasks that consume time without requiring clinical judgment, and escalate when the situation calls for a human.

• Patient scheduling and intake: Books, reschedules, and cancels appointments by checking live availability and confirming details without staff involvement.
• Insurance and billing support: Answers eligibility and coverage questions instantly, reducing the queue for billing representatives on common, repeatable inquiries.
• Human escalation: When a situation involves clinical sensitivity or patient distress, the agent transfers to a nurse or care coordinator with full context already attached.

3. AI Virtual Agents in Financial Services

Financial services firms operate under strict compliance requirements and high interaction volumes, two conditions that make manual oversight impractical at scale. AI agents address both by monitoring transactions and conversations continuously rather than in periodic spot checks.

• Fraud detection and risk management: Monitors transaction patterns and flags anomalies as they occur, triggering a compliance workflow without waiting for a manual review cycle.
• 100% interaction monitoring: Scores every conversation against regulatory standards like FDCPA, closing the gap left by human QA teams that can realistically review only 1-2% of calls.
• Agent screen monitoring: Captures desktop activity during and after calls alongside conversation analysis, so QA teams see what agents did, not just what they said.

4. AI Virtual Agents in Contact Centers

Contact centers generate large volumes of repetitive work that sits outside the conversation itself, documentation, scoring, and coaching. AI agents handle that work automatically, which reduces handle time and gives managers better data to act on.

• Post-call automation: Generates call summaries, populates CRM fields, and creates follow-up tasks after every interaction, removing the manual data entry that extends handle time and delays reporting.
• Personalized coaching: Identifies performance gaps from real conversations and surfaces coaching recommendations tied to actual behavior, not observations from a small sample of reviewed calls.
• Conversation scoring and FDCPA compliance: Scores every conversation against custom quality standards and flags compliance violations as they occur, rather than after a complaint has already been filed.

5. AI Virtual Agents in Telecom

Telecom providers handle some of the highest contact volumes of any industry, with a large share of those contacts covering billing disputes, service outages, and plan changes. Most of those requests follow predictable patterns, which makes them well-suited for AI agents that can pull account data, check network status, and make changes without routing to a live rep.

• Billing and account management: Resolves billing questions, applies credits, and processes plan changes by connecting directly to billing and account management systems, without putting the customer on hold.
• Outage detection and updates: Identifies whether a customer's issue is tied to a known network outage, pulls live status data, and gives the customer an accurate update rather than opening an unnecessary technical support ticket.
• Device and service troubleshooting: Walks customers through diagnostic steps for connectivity or device issues, and escalates to a technical rep with full context when the issue requires hands-on intervention.

Frequently Asked Questions

1. What are real-world use cases of AI agents in enterprises?

AI agents are used for IT support, customer service automation, sales lead qualification, HR onboarding, and finance workflow automation. Unlike chatbots, agentic AI systems can take action across tools, resolve tickets, and trigger workflows without constant human input.

2. How do enterprises measure ROI from agentic AI initiatives?

Enterprises measure ROI through reduced resolution time, lower operational costs, improved CSAT, increased agent productivity, and faster ticket deflection. Many organizations also track workflow automation rates and revenue impact from improved customer experience.

3. What governance structures are needed to deploy agentic AI safely?

Organizations need human-in-the-loop controls, clear escalation paths, role-based access, audit logs, and compliance monitoring. Governance frameworks should define when AI can act autonomously and when human approval is required.

4. Can agentic AI work alongside existing automation tools like RPA or iPaaS?

Yes. Agentic AI complements RPA and iPaaS by adding intelligence and decision-making. While RPA handles rule-based tasks, AI agents can interpret context, adapt to changes, and orchestrate multi-system workflows dynamically.

5. What technical prerequisites should organizations meet before adopting agentic AI?

Companies should have clean data pipelines, API-enabled systems, defined workflows, and clear success metrics. Integration readiness and strong data governance significantly improve deployment success.

6. What tasks can AI agents perform autonomously?

AI agents can triage tickets, summarize conversations, resolve common support issues, trigger backend processes, escalate complex cases, and generate reports. Advanced agents can coordinate across multiple enterprise systems without manual intervention.

7. How will agentic AI evolve over the next few years?

Agentic AI will move from assistive copilots to fully orchestrated digital teammates capable of handling end-to-end workflows. Expect stronger multi-agent collaboration, better reasoning, deeper system integrations, and tighter compliance controls.

Wrapping up: Choosing the Right AI Agent Platform

AI agents produce reliable results when three conditions are met: complete interaction data, intent detection built on NLU rather than keyword matching, and clean integration with existing infrastructure. Without all three, coverage gaps and accuracy problems limit what the platform can actually deliver at scale.

This guide covered the core use cases for AI agents in contact centers - self-service, live agent assistance, automated QA, inferred satisfaction scoring, and after-call documentation - along with the buying criteria and a comparison of five platforms.

Level AI meets all three conditions. Beyond the capabilities covered in this guide, the platform also includes:

• Agent coaching: Identifies performance gaps in real conversations, prioritizes what to address first, and tracks improvement over time
• Screen recording: Captures agent desktop activity during and after a call alongside conversation analysis, so QA teams see what agents did, not just what they said
• Conversation analytics dashboards: Delivers a unified view of performance data for QA teams, agents, and team leaders, built on 100% of interactions

If you want to evaluate how Level AI performs on your contact center's conversations, click here to schedule a demo.